





import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 # to overwrite at feature extractactor specific tests
 lowercase_						=     None
 lowercase_						=     None

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Dict:



   '''simple docstring'''



   return self.feat_extract_tester.prepare_feat_extract_dict()

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->int:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.feature_extraction_class(**self.feat_extract_dict)
   self.assertTrue(hasattr(_A			,					"feature_size"))
   self.assertTrue(hasattr(_A			,					"sampling_rate"))
   self.assertTrue(hasattr(_A			,					"padding_value"))

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.feat_extract_tester.prepare_inputs_for_common()
   lowerCamelCase__: Optional[int]         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Optional[Any]         =feat_extract.model_input_names[0]

   lowerCamelCase__: int         =BatchFeature({input_name: speech_inputs})

   self.assertTrue(all(len(_A) == len(_A) for x, y in zip(_A			,					processed_features[input_name])))

   lowerCamelCase__: str         =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_A)
   lowerCamelCase__: Optional[Any]         =BatchFeature({input_name: speech_inputs}			,					tensor_type="np")

   lowerCamelCase__: str         =processed_features[input_name]

   if len(batch_features_input.shape) < 3:
     lowerCamelCase__: Dict         =batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

 @require_torch
 def 				SCREAMING_SNAKE_CASE_   (self							:  List[str])			->List[Any]:



   '''simple docstring'''



   lowerCamelCase__: Dict         =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_A)
   lowerCamelCase__: List[str]         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Optional[int]         =feat_extract.model_input_names[0]

   lowerCamelCase__: Tuple         =BatchFeature({input_name: speech_inputs}			,					tensor_type="pt")

   lowerCamelCase__: str         =processed_features[input_name]

   if len(batch_features_input.shape) < 3:
     lowerCamelCase__: Any         =batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

 @require_tf
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: str         =self.feat_extract_tester.prepare_inputs_for_common(equal_length=_A)
   lowerCamelCase__: Optional[Any]         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Union[str, Any]         =feat_extract.model_input_names[0]

   lowerCamelCase__: Optional[int]         =BatchFeature({input_name: speech_inputs}			,					tensor_type="tf")

   lowerCamelCase__: Union[str, Any]         =processed_features[input_name]

   if len(batch_features_input.shape) < 3:
     lowerCamelCase__: Optional[int]         =batch_features_input[:, :, None]

   self.assertTrue(
       batch_features_input.shape
       == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.feature_size))

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  List[Any]=False)			->Optional[int]:



   '''simple docstring'''



   def _inputs_have_equal_length(UpperCAmelCase_							:  Any):
     lowerCamelCase__: Dict         =len(input[0])
     for input_slice in input[1:]:
       if len(_A) != length:
         return False
     return True

   def _inputs_are_equal(UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Union[str, Any]):
     if len(_A) != len(_A):
       return False

     for input_slice_a, input_slice_a in zip(_A			,					_A):
       if not np.allclose(np.asarray(_A)			,					np.asarray(_A)			,					atol=1E-3):
         return False
     return True

   lowerCamelCase__: Optional[Any]         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: List[str]         =self.feat_extract_tester.prepare_inputs_for_common(numpify=_A)
   lowerCamelCase__: str         =feat_extract.model_input_names[0]

   lowerCamelCase__: Union[str, Any]         =BatchFeature({input_name: speech_inputs})

   lowerCamelCase__: Any         =self.feat_extract_tester.seq_length_diff
   lowerCamelCase__: Tuple         =self.feat_extract_tester.max_seq_length + pad_diff
   lowerCamelCase__: Optional[int]         =self.feat_extract_tester.min_seq_length
   lowerCamelCase__: List[str]         =self.feat_extract_tester.batch_size
   lowerCamelCase__: Tuple         =self.feat_extract_tester.feature_size

   # test padding for List[int] + numpy
   lowerCamelCase__: Any         =feat_extract.pad(_A			,					padding=_A)
   lowerCamelCase__: List[Any]         =input_a[input_name]

   lowerCamelCase__: int         =feat_extract.pad(_A			,					padding="longest")
   lowerCamelCase__: Optional[int]         =input_a[input_name]

   lowerCamelCase__: str         =feat_extract.pad(_A			,					padding="max_length"			,					max_length=len(speech_inputs[-1]))
   lowerCamelCase__: int         =input_a[input_name]

   lowerCamelCase__: List[Any]         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="np")
   lowerCamelCase__: Dict         =input_a[input_name]

   # max_length parameter has to be provided when setting `padding="max_length"`
   with self.assertRaises(_A):
     feat_extract.pad(_A			,					padding="max_length")[input_name]

   lowerCamelCase__: Any         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=_A			,					return_tensors="np")
   lowerCamelCase__: List[Any]         =input_a[input_name]

   self.assertFalse(_inputs_have_equal_length(_A))
   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertTrue(_inputs_are_equal(_A			,					_A))
   self.assertTrue(len(input_a[0]) == pad_min_length)
   self.assertTrue(len(input_a[1]) == pad_min_length + pad_diff)
   self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0])))
   self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length))

   if feature_size > 1:
     self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size)

   # test padding for `pad_to_multiple_of` for List[int] + numpy
   lowerCamelCase__: List[Any]         =feat_extract.pad(_A			,					pad_to_multiple_of=10)
   lowerCamelCase__: Tuple         =input_a[input_name]

   lowerCamelCase__: Tuple         =feat_extract.pad(_A			,					padding="longest"			,					pad_to_multiple_of=10)
   lowerCamelCase__: Union[str, Any]         =input_a[input_name]

   lowerCamelCase__: Any         =feat_extract.pad(
       _A			,					padding="max_length"			,					pad_to_multiple_of=10			,					max_length=_A)
   lowerCamelCase__: Dict         =input_a[input_name]

   lowerCamelCase__: Union[str, Any]         =feat_extract.pad(
       _A			,					padding="max_length"			,					pad_to_multiple_of=10			,					max_length=_A			,					return_tensors="np"			,					)
   lowerCamelCase__: Any         =input_a[input_name]

   self.assertTrue(all(len(_A) % 10 == 0 for x in input_a))
   self.assertTrue(_inputs_are_equal(_A			,					_A))

   lowerCamelCase__: Dict         =pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
   self.assertTrue(all(len(_A) == expected_mult_pad_length for x in input_a))
   self.assertEqual(input_a.shape[:2]			,					(batch_size, expected_mult_pad_length))

   if feature_size > 1:
     self.assertTrue(input_a.shape[2] == feature_size)

   # Check padding value is correct
   lowerCamelCase__: Optional[int]         =(np.ones(self.feat_extract_tester.feature_size) * feat_extract.padding_value).sum()
   self.assertTrue(
       abs(np.asarray(input_a[0])[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length))
       < 1E-3)
   self.assertTrue(
       abs(
           np.asarray(input_a[1])[pad_min_length + pad_diff :].sum()
           - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff))
       < 1E-3)
   self.assertTrue(
       abs(
           np.asarray(input_a[2])[pad_min_length + 2 * pad_diff :].sum()
           - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff))
       < 1E-3)
   self.assertTrue(
       abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length)) < 1E-3)
   self.assertTrue(
       abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length))
       < 1E-3)

 def 				SCREAMING_SNAKE_CASE_   (self							:  str			,					UpperCAmelCase_							:  List[str]=False)			->str:



   '''simple docstring'''



   def _inputs_have_equal_length(UpperCAmelCase_							:  int):
     lowerCamelCase__: Dict         =len(input[0])
     for input_slice in input[1:]:
       if len(_A) != length:
         return False
     return True

   def _inputs_are_equal(UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Dict):
     if len(_A) != len(_A):
       return False

     for input_slice_a, input_slice_a in zip(_A			,					_A):
       if not np.allclose(np.asarray(_A)			,					np.asarray(_A)			,					atol=1E-3):
         return False
     return True

   lowerCamelCase__: Dict         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Union[str, Any]         =self.feat_extract_tester.prepare_inputs_for_common(numpify=_A)
   lowerCamelCase__: List[str]         =feat_extract.model_input_names[0]

   lowerCamelCase__: Optional[int]         =BatchFeature({input_name: speech_inputs})

   # truncate to smallest
   lowerCamelCase__: List[str]         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[0])			,					truncation=_A)
   lowerCamelCase__: str         =input_a[input_name]

   lowerCamelCase__: Optional[Any]         =feat_extract.pad(_A			,					padding="max_length"			,					max_length=len(speech_inputs[0]))
   lowerCamelCase__: Tuple         =input_a[input_name]

   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertFalse(_inputs_have_equal_length(_A))

   # truncate to smallest with np
   lowerCamelCase__: str         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[0])			,					return_tensors="np"			,					truncation=_A			,					)
   lowerCamelCase__: Tuple         =input_a[input_name]

   lowerCamelCase__: List[Any]         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[0])			,					return_tensors="np")
   lowerCamelCase__: Union[str, Any]         =input_a[input_name]

   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertTrue(input_a.shape[1] == len(speech_inputs[0]))

   # since truncation forces padding to be smaller than longest input
   # function can't return `np.ndarray`, but has to return list
   self.assertFalse(_inputs_have_equal_length(_A))

   # truncate to middle
   lowerCamelCase__: Any         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[1])			,					truncation=_A			,					return_tensors="np"			,					)
   lowerCamelCase__: Optional[Any]         =input_a[input_name]

   lowerCamelCase__: Any         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[1])			,					truncation=_A)
   lowerCamelCase__: Union[str, Any]         =input_a[input_name]

   lowerCamelCase__: List[Any]         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[1])			,					return_tensors="np")
   lowerCamelCase__: Dict         =input_a[input_name]

   self.assertTrue(input_a.shape[1] == len(speech_inputs[1]))
   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertTrue(_inputs_are_equal(_A			,					_A))

   # since truncation forces padding to be smaller than longest input
   # function can't return `np.ndarray`, but has to return list
   self.assertFalse(_inputs_have_equal_length(_A))
   self.assertTrue(len(input_a[-1]) == len(speech_inputs[-1]))

   # padding has to be max_length when setting `truncation=True`
   with self.assertRaises(_A):
     feat_extract.pad(_A			,					truncation=_A)[input_name]

   # padding has to be max_length when setting `truncation=True`
   with self.assertRaises(_A):
     feat_extract.pad(_A			,					padding="longest"			,					truncation=_A)[input_name]

   # padding has to be max_length when setting `truncation=True`
   with self.assertRaises(_A):
     feat_extract.pad(_A			,					padding="longest"			,					truncation=_A)[input_name]

   # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
   with self.assertRaises(_A):
     feat_extract.pad(_A			,					padding="max_length"			,					truncation=_A)[input_name]

   # test truncation for `pad_to_multiple_of` for List[int] + numpy
   lowerCamelCase__: Dict         =12
   lowerCamelCase__: str         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[0])			,					pad_to_multiple_of=_A			,					truncation=_A			,					)
   lowerCamelCase__: Optional[int]         =input_a[input_name]

   lowerCamelCase__: str         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=len(speech_inputs[0])			,					pad_to_multiple_of=_A			,					)
   lowerCamelCase__: Optional[Any]         =input_a[input_name]

   # retrieve expected_length as multiple of pad_to_multiple_of
   lowerCamelCase__: Tuple         =len(speech_inputs[0])
   if expected_length % pad_to_multiple_of != 0:
     lowerCamelCase__: Tuple         =((len(speech_inputs[0]) // pad_to_multiple_of) + 1) * pad_to_multiple_of

   self.assertTrue(len(input_a[0]) == expected_length)
   self.assertTrue(_inputs_have_equal_length(_A))
   self.assertFalse(_inputs_have_equal_length(_A))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Union[str, Any]:



   '''simple docstring'''



   self._check_padding(numpify=_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->Tuple:



   '''simple docstring'''



   self._check_padding(numpify=_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple)			->Any:



   '''simple docstring'''



   self._check_truncation(numpify=_A)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Union[str, Any])			->int:



   '''simple docstring'''



   self._check_truncation(numpify=_A)

 @require_torch
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Tuple         =self.feat_extract_tester.prepare_inputs_for_common()
   lowerCamelCase__: Optional[Any]         =feat_extract.model_input_names[0]

   lowerCamelCase__: str         =BatchFeature({input_name: speech_inputs})

   lowerCamelCase__: Dict         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="np")[input_name]
   lowerCamelCase__: List[Any]         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="pt")[input_name]

   self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1E-2)

 @require_tf
 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict)			->Dict:



   '''simple docstring'''



   lowerCamelCase__: Any         =self.feature_extraction_class(**self.feat_extract_dict)
   lowerCamelCase__: Optional[Any]         =self.feat_extract_tester.prepare_inputs_for_common()
   lowerCamelCase__: Any         =feat_extract.model_input_names[0]

   lowerCamelCase__: str         =BatchFeature({input_name: speech_inputs})

   lowerCamelCase__: Any         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="np")[input_name]
   lowerCamelCase__: List[Any]         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="tf")[input_name]

   self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_tf.numpy().astype(np.floataa).sum()) < 1E-2)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[int])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =self.feat_extract_dict
   lowerCamelCase__: Union[str, Any]         =True
   lowerCamelCase__: str         =self.feature_extraction_class(**_A)
   lowerCamelCase__: int         =self.feat_extract_tester.prepare_inputs_for_common()
   lowerCamelCase__: List[Any]         =[len(_A) for x in speech_inputs]
   lowerCamelCase__: Union[str, Any]         =feat_extract.model_input_names[0]

   lowerCamelCase__: str         =BatchFeature({input_name: speech_inputs})

   lowerCamelCase__: List[str]         =feat_extract.pad(_A			,					padding="longest"			,					return_tensors="np")
   self.assertIn("attention_mask"			,					_A)
   self.assertListEqual(list(processed.attention_mask.shape)			,					list(processed[input_name].shape[:2]))
   self.assertListEqual(processed.attention_mask.sum(-1).tolist()			,					_A)







 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =self.feat_extract_dict
   lowerCamelCase__: Dict         =True
   lowerCamelCase__: str         =self.feature_extraction_class(**_A)
   lowerCamelCase__: Dict         =self.feat_extract_tester.prepare_inputs_for_common()
   lowerCamelCase__: List[str]         =[len(_A) for x in speech_inputs]
   lowerCamelCase__: List[str]         =feat_extract.model_input_names[0]

   lowerCamelCase__: Optional[int]         =BatchFeature({input_name: speech_inputs})
   lowerCamelCase__: Any         =min(_A)

   lowerCamelCase__: Union[str, Any]         =feat_extract.pad(
       _A			,					padding="max_length"			,					max_length=_A			,					truncation=_A			,					return_tensors="np")
   self.assertIn("attention_mask"			,					_A)
   self.assertListEqual(
       list(processed_pad.attention_mask.shape)			,					[processed_pad[input_name].shape[0], max_length])
   self.assertListEqual(
       processed_pad.attention_mask[:, :max_length].sum(-1).tolist()			,					[max_length for x in speech_inputs])









'''simple docstring'''





import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


_UpperCamelCase						:						int  =					16
_UpperCamelCase						:						int  =					32
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Optional[Any]:
			return int(x / 2**2_0		)


class  snake_case__      :




					def __enter__(  self  :		List[Any]      )    ->							str:
								gc.collect()
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
								UpperCAmelCase_      :       Dict          =       torch.cuda.memory_allocated()
								return self





					def __exit__(  self  :		Optional[Any]				, *_A  :		Any      )    ->							Any:
								gc.collect()
								torch.cuda.empty_cache()
								UpperCAmelCase_      :       Optional[int]          =       torch.cuda.memory_allocated()
								UpperCAmelCase_      :       int          =       torch.cuda.max_memory_allocated()
								UpperCAmelCase_      :       int          =       bamb(self.end - self.begin      )
								UpperCAmelCase_      :       List[str]          =       bamb(self.peak - self.begin      )
								# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def __UpperCAmelCase   (					A					:			Accelerator			,       A					:			int = 1_6			,       A					:			str = "bert-base-cased"			,       A					:			int = 3_2_0			,       A					:			int = 1_6_0			,       )    ->   Dict:
			UpperCAmelCase_      :       List[str]          =       AutoTokenizer.from_pretrained(A		)
			UpperCAmelCase_      :       List[str]          =       load_dataset(
			    '''glue'''			,       '''mrpc'''			,       split={'''train''': F"train[:{n_train}]", '''validation''': F"validation[:{n_val}]"}		)

			def tokenize_function(A					:			int		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_      :       Optional[int]          =       tokenizer(examples['''sentence1''']			,       examples['''sentence2''']			,       truncation=A			,       max_length=A		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_      :       Union[str, Any]          =       datasets.map(
			    A			,       batched=A			,       remove_columns=['''idx''', '''sentence1''', '''sentence2''']			,       load_from_cache_file=A		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_      :       Optional[int]          =       tokenized_datasets.rename_column('''label'''			,       '''labels'''		)

			def collate_fn(A					:			List[Any]		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A			,       padding='''max_length'''			,       max_length=1_2_8			,       return_tensors='''pt'''		)
						return tokenizer.pad(A			,       padding='''longest'''			,       return_tensors='''pt'''		)

			# Instantiate dataloaders.
			UpperCAmelCase_      :       Optional[Any]          =       DataLoader(
			    tokenized_datasets['''train''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)
			UpperCAmelCase_      :       List[Any]          =       DataLoader(
			    tokenized_datasets['''validation''']			,       shuffle=A			,       collate_fn=A			,       batch_size=A		)

			return train_dataloader, eval_dataloader
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   Dict:
			# Initialize accelerator
			UpperCAmelCase_      :       Dict          =       Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_      :       Optional[int]          =       config['''lr''']
			UpperCAmelCase_      :       List[Any]          =       int(config['''num_epochs''']		)
			UpperCAmelCase_      :       Any          =       int(config['''seed''']		)
			UpperCAmelCase_      :       List[str]          =       int(config['''batch_size''']		)
			UpperCAmelCase_      :       Tuple          =       args.model_name_or_path

			set_seed(A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       get_dataloaders(A			,       A			,       A			,       args.n_train			,       args.n_val		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_      :       Optional[Any]          =       AutoModelForSequenceClassification.from_pretrained(A			,       return_dict=A		)

			# Instantiate optimizer
			UpperCAmelCase_      :       str          =       (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_      :       Optional[Any]          =       optimizer_cls(params=model.parameters()			,       lr=A		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_      :       Tuple          =       accelerator.state.deepspeed_plugin.deepspeed_config[
						    '''gradient_accumulation_steps'''
						]
			else:
						UpperCAmelCase_      :       Tuple          =       1
			UpperCAmelCase_      :       List[Any]          =       (len(A		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_      :       List[str]          =       get_linear_schedule_with_warmup(
						    optimizer=A			,       num_warmup_steps=0			,       num_training_steps=A			,       )
			else:
						UpperCAmelCase_      :       Optional[Any]          =       DummyScheduler(A			,       total_num_steps=A			,       warmup_num_steps=0		)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.
			UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       accelerator.prepare(
			    A			,       A			,       A			,       A			,       A		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_      :       Optional[Any]          =       0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_      :       List[str]          =       0

			# Now we train the model
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for epoch in range(A			,       A		):
						with TorchTracemalloc() as tracemalloc:
									model.train()
									for step, batch in enumerate(A		):
												UpperCAmelCase_      :       List[Any]          =       model(**A		)
												UpperCAmelCase_      :       List[str]          =       outputs.loss
												UpperCAmelCase_      :       Union[str, Any]          =       loss / gradient_accumulation_steps
												accelerator.backward(A		)
												if step % gradient_accumulation_steps == 0:
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

												overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
						accelerator.print('''Memory before entering the train : {}'''.format(bamb(tracemalloc.begin		)		)		)
						accelerator.print('''Memory consumed at the end of the train (end-begin): {}'''.format(tracemalloc.used		)		)
						accelerator.print('''Peak Memory consumed during the train (max-begin): {}'''.format(tracemalloc.peaked		)		)
						accelerator.print(
						    '''Total Peak Memory consumed during the train (max): {}'''.format(
						        tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
						UpperCAmelCase_      :       List[str]          =       tracemalloc.peaked + bamb(tracemalloc.begin		)
						if args.peak_memory_upper_bound is not None:
									assert (
									    train_total_peak_memory[F"epoch-{epoch}"] <= args.peak_memory_upper_bound
									), "Peak memory usage exceeded the upper bound"

			accelerator.wait_for_everyone()
			if accelerator.is_main_process:
						with open(os.path.join(args.output_dir			,       '''peak_memory_utilization.json'''		)			,       '''w'''		) as f:
									json.dump(A			,       A		)
def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       Dict          =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
			parser.add_argument(
			    '''--model_name_or_path'''			,       type=A			,       default='''bert-base-cased'''			,       help='''Path to pretrained model or model identifier from huggingface.co/models.'''			,       required=A			,       )
			parser.add_argument(
			    '''--output_dir'''			,       type=A			,       default='''.'''			,       help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''			,       )
			parser.add_argument(
			    '''--peak_memory_upper_bound'''			,       type=A			,       default=A			,       help='''The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value.'''			,       )
			parser.add_argument(
			    '''--n_train'''			,       type=A			,       default=3_2_0			,       help='''Number of training examples to use.'''			,       )
			parser.add_argument(
			    '''--n_val'''			,       type=A			,       default=1_6_0			,       help='''Number of validation examples to use.'''			,       )
			parser.add_argument(
			    '''--num_epochs'''			,       type=A			,       default=1			,       help='''Number of train epochs.'''			,       )
			UpperCAmelCase_      :       Union[str, Any]          =       parser.parse_args()
			UpperCAmelCase_      :       List[Any]          =       {'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}
			training_function(A			,       A		)


if __name__ == "__main__":
						main()


